regression-impact

Quantify change impact with controlled before/after comparisons and statistical significance tests.

13|4|Updated Mar 14, 2026

Search Tags:#evidence #regression #data-analysis #impact-analysis #statistical-significance #before-after

Ready to run

npx skills add https://github.com/henrique-simoes/Istara --skill regression-impact

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: regression-impact
Source: https://github.com/henrique-simoes/Istara/tree/main/skills/deliver/regression-impact
Command: npx skills add https://github.com/henrique-simoes/Istara --skill regression-impact

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Before/after comparisons are essential to quantify the impact of changes. This Skill guides teams to measure changes, control for confounders, and determine statistical significance so decision-makers can act with confidence.

Core Features & Use Cases

Plan and execute controlled comparisons to isolate the effect of changes.
Compute effect sizes, p-values, and confidence intervals to assess significance.
Generate structured reports linking findings to actions and prioritizing recommendations.
Use Case: A product team ships a UI tweak and wants to quantify its effect on task completion time and conversion rate.

Quick Start

Run a regression-impact analysis on your recent change by providing project data and outcome metrics.

Frequently Asked Questions about regression-impact

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I measure the impact of a product change using before/after data?▼

Before/after comparisons quantify the impact of product changes by comparing outcome metrics before and after deployment. This approach isolates the effect of changes while controlling for confounders to determine statistical significance and practical impact.

What is the best way to determine statistical significance in a UX change?▼

Determining statistical significance for a UX change requires computing effect sizes, p-values, and confidence intervals. This process evaluates whether observed metric differences result from the change itself or random variation, providing confidence scoring for decision-makers.

Can I use controlled before/after comparisons for process optimization?▼

Controlled before/after comparisons apply directly to process optimization by measuring outcome metrics across projects. They isolate the effect of process changes, compute effect sizes, and generate structured reports linking findings to prioritized recommendations.

How do I calculate effect sizes and confidence intervals for a recent change?▼

Calculating effect sizes and confidence intervals involves running a regression-impact analysis with project data and outcome metrics. The analysis computes p-values and confidence intervals to assess the statistical significance and practical impact of the change.

Why do I need hypothesis testing when analyzing a UI tweak's effect on conversion rate?▼

Hypothesis testing is needed when analyzing a UI tweak's effect on conversion rate to ensure evidence-backed reporting. It controls for confounders and determines whether observed differences in task completion time or conversion are statistically significant rather than coincidental.

What data do I need to run an impact analysis on recent project changes?▼

Running an impact analysis on recent project changes requires providing project data and outcome metrics. This input allows the analysis to perform controlled comparisons, compute effect sizes, and generate structured reports with confidence scoring and prioritized recommendations.